Safety classification using GIS in decision-making process to define priority road interventions

Given the current economic situation of the Portuguese municipalities, it is necessary to identify the priority investments in order to achieve a more efficient financial management. The classification of the road network of the municipality according to the occurrence of traffic accidents is fundamental to set priorities for road interventions. This paper presents a model for road network classification based on traffic accidents integrated in a geographic information system. Its practical application was developed through a case study in the municipality of Barcelos. An equation was defined to obtain a road safety index through the combination of the following indicators: severity, property damage only and accident costs. In addition to the road network classification, the application of the model allows to analyze the spatial coverage of accidents in order to determine the centrality and dispersion of the locations with the highest incidence of road accidents. This analysis can be further refined according to the nature of the accidents namely in collision, runoff and pedestrian crashes.

An evolutionary multi-objective optimization system for earthworks

Earthworks involve the levelling or shaping of a target area through the moving or processing of the ground surface. Most construction projects require earthworks, which are heavily dependent on mechanical equipment (e.g., excavators, trucks and compactors). Often, earthworks are the most costly and time-consuming component of infrastructure constructions (e.g., road, railway and airports) and current pressure for higher productivity and safety highlights the need to optimize earthworks, which is a nontrivial task. Most previous attempts at tackling this problem focus on single-objective optimization of partial processes or aspects of earthworks, overlooking the advantages of a multi-objective and global optimization. This work describes a novel optimization system based on an evolutionary multi-objective approach, capable of globally optimizing several objectives simultaneously and dynamically. The proposed system views an earthwork construction as a production line, where the goal is to optimize resources under two crucial criteria (costs and duration) and focus the evolutionary search (non-dominated sorting genetic algorithm-II) on compaction allocation, using linear programming to distribute the remaining equipment (e.g., excavators). Several experiments were held using real-world data from a Portuguese construction site, showing that the proposed system is quite competitive when compared with current manual earthwork equipment allocation.

Comparison of single and multi-objective evolutionary algorithms for robust link-state routing

Traffic Engineering (TE) approaches are increasingly impor- tant in network management to allow an optimized configuration and resource allocation. In link-state routing, the task of setting appropriate weights to the links is both an important and a challenging optimization task. A number of different approaches has been put forward towards this aim, including the successful use of Evolutionary Algorithms (EAs). In this context, this work addresses the evaluation of three distinct EAs, a single and two multi-objective EAs, in two tasks related to weight setting optimization towards optimal intra-domain routing, knowing the network topology and aggregated traffic demands and seeking to mini- mize network congestion. In both tasks, the optimization considers sce- narios where there is a dynamic alteration in the state of the system, in the first considering changes in the traffic demand matrices and in the latter considering the possibility of link failures. The methods will, thus, need to simultaneously optimize for both conditions, the normal and the altered one, following a preventive TE approach towards robust configurations. Since this can be formulated as a bi-objective function, the use of multi-objective EAs, such as SPEA2 and NSGA-II, came nat- urally, being those compared to a single-objective EA. The results show a remarkable behavior of NSGA-II in all proposed tasks scaling well for harder instances, and thus presenting itself as the most promising option for TE in these scenarios.

Combining non-dominance, objective-order and spread metric to extend firefly algorithm to multi-objective optimization

In this paper, we propose an extension of the firefly algorithm (FA) to multi-objective optimization. FA is a swarm intelligence optimization algorithm inspired by the flashing behavior of fireflies at night that is capable of computing global solutions to continuous optimization problems. Our proposal relies on a fitness assignment scheme that gives lower fitness values to the positions of fireflies that correspond to non-dominated points with smaller aggregation of objective function distances to the minimum values. Furthermore, FA randomness is based on the spread metric to reduce the gaps between consecutive non-dominated solutions. The obtained results from the preliminary computational experiments show that our proposal gives a dense and well distributed approximated Pareto front with a large number of points.

Metabolic engineering with multi-objective optimization of kinetic models

Kinetic models have a great potential for metabolic engineering applications. They can be used for testing which genetic and regulatory modifications can increase the production of metabolites of interest, while simultaneously monitoring other key functions of the host organism. This work presents a methodology for increasing productivity in biotechnological processes exploiting dynamic models. It uses multi-objective dynamic optimization to identify the combination of targets (enzymatic modifications) and the degree of up- or down-regulation that must be performed in order to optimize a set of pre-defined performance metrics subject to process constraints. The capabilities of the approach are demonstrated on a realistic and computationally challenging application: a large-scale metabolic model of Chinese Hamster Ovary cells (CHO), which are used for antibody production in a fed-batch process. The proposed methodology manages to provide a sustained and robust growth in CHO cells, increasing productivity while simultaneously increasing biomass production, product titer, and keeping the concentrations of lactate and ammonia at low values. The approach presented here can be used for optimizing metabolic models by finding the best combination of targets and their optimal level of up/down-regulation. Furthermore, it can accommodate additional trade-offs and constraints with great flexibility.

Analyzing university spin-offs and supporting decision making in new venture creation

Magdeburg, Univ., Fak. für Wirtschaftswiss., Diss., 2008

Network Meta-analysis to Synthesize Evidence for Decision Making in Cardiovascular Research

Abstract Clinical decision-making requires synthesis of evidence from literature reviews focused on a specific theme. Evidence synthesis is performed with qualitative assessments and systematic reviews of randomized clinical trials, typically covering statistical pooling with pairwise meta-analyses. These methods include adjusted indirect comparison meta-analysis, network meta-analysis, and mixed-treatment comparison. These tools allow synthesis of evidence and comparison of effectiveness in cardiovascular research.

Incentive structures in economic experiments : a neuroeconomic analysis of decision making under risk

Magdeburg, Univ., Fak. für Wirtschaftswiss., Diss., 2013

Medical decision making: perspectives of patients and physicians

Magdeburg, Univ., Fak. für Wirtschaftswiss., Diss., 2013

Cooperative vs. non-cooperative mechanisms in the EU decision making process

According to the account of the European Union (EU) decision making proposed in this paper, this is a bargaining process during which actors shift their policy positions with a view to reaching agreements on controversial issues.

Women's decision making and experience of subsequent pregnancy following stillbirth.

INTRODUCTION: This study sought to increase understanding of women's thoughts and feelings about decision making and the experience of subsequent pregnancy following stillbirth (intrauterine death after 24 weeks' gestation). METHODS: Eleven women were interviewed, 8 of whom were pregnant at the time of the interview. Modified grounded theory was used to guide the research methodology and to analyze the data. RESULTS: A model was developed to illustrate women's experiences of decision making in relation to subsequent pregnancy and of subsequent pregnancy itself. DISCUSSION: The results of the current study have significant implications for women who have experienced stillbirth and the health professionals who work with them. Based on the model, women may find it helpful to discuss their beliefs in relation to healing and health professionals to provide support with this in mind. Women and their partners may also benefit from explanations and support about the potentially conflicting emotions they may experience during this time.

Policy Labels and Investment Decision-making

Much attention in recent years has turned to the potential of behavioural insights to improve the performance of government policy. One behavioural concept of interest is the effect of a cash transfer label on how the transfer is spent. The Winter Fuel Payment (WFP) is a labelled cash transfer to offset the costs of keeping older households warm in the winter. Previous research has shown that households spend a higher proportion of the WFP on energy expenditures due to its label (Beatty et al., 2011). If households interpret the WFP as money for their energy bills, it may reduce their willingness to undertake investments which help achieving the same goal, such as the adoption of renewable energy technologies. In this paper we show that the WFP has distortionary effects on the renewable technology market. Using the sharp eligibility criteria of the WFP in a Regression Discontinuity Design, this analysis finds a reduction in the propensity to install renewable energy technologies of around 2.7 percentage points due to the WFP. This is a considerable number. It implies that 62% of households (whose oldest member turns 60) would have invested in renewable energy but refrain to do so after receiving the WFP. This analysis suggests that the labelling effect spreads to products related to the labelled good. In this case, households use too much energy from sources which generate pollution and too little from relatively cleaner technologies.

Effects of stress on economic decision-making: Evidence from laboratory experiments

The ways in which preferences respond to the varying stress of economic environments is a key question for behavioral economics and public policy. We conducted a laboratory experiment to investigate the effects of stress on financial decision making among individuals aged 50 and older. Using the cold pressor task as a physiological stressor, and a series of intelligence tests as cognitive stressors, we find that stress increases subjective discounting rates, has no effect on the degree of risk-aversion, and substantially lowers the effort individuals make to learn about financial decisions.

Effects of anxiety on visuospatial pattern discrimination and decision-making processes: what can we learn from brain dynamics ?

Detection and discrimination of visuospatial input involve at least extracting, selecting and encoding relevant information and decision-making processes allowing selecting a response. These two operations are altered, respectively, by attentional mechanisms that change discrimination capacities, and by beliefs concerning the likelihood of uncertain events. Information processing is tuned by the attentional level that acts like a filter on perception, while decision-making processes are weighed by subjective probability of risk. In addition, it has been shown that anxiety could affect the detection of unexpected events through the modification of the level of arousal. Consequently, purpose of this study concerns whether and how decision-making and brain dynamics are affected by anxiety. To investigate these questions, the performance of women with either a high (12) or a low (12) STAI-T (State-Trait Anxiety Inventory, Spielberger, 1983) was examined in a decision-making visuospatial task where subjects have to recognize a target visual pattern from non-target patterns. The target pattern was a schematic image of furniture arranged in such a way as to give the impression of a living room. Non-target patterns were created by either the compression or the dilatation of the distances between objects. Target and non-target patterns were always presented in the same configuration. Preliminary behavioral results show no group difference in reaction time. In addition, visuo-spatial abilities were analyzed trough the signal detection theory for quantifying perceptual decisions in the presence of uncertainty (Green and Swets, 1966). This theory treats detection of a stimulus as a decision-making process determined by the nature of the stimulus and cognitive factors. Astonishingly, no difference in d' (corresponding to the distance between means of the distributions) and c (corresponds to the likelihood ratio) indexes was observed. Comparison of Event-related potentials (ERP) reveals that brain dynamics differ according to anxiety. It shows differences in component latencies, particularly a delay in anxious subjects over posterior electrode sites. However, these differences are compensated during later components by shorter latencies in anxious subjects compared to non-anxious one. These inverted effects seem indicate that the absence of difference in reaction time rely on a compensation of attentional level that tunes cortical activation in anxious subjects, but they have to hammer away to maintain performance.